Introduction: Testing for active SARS-CoV-2 infection is a fundamental tool in the public health measures taken to control the COVID-19 pandemic. Because of the overwhelming use of SARS-CoV-2 reverse transcription (RT)-PCR tests worldwide, the availability of test kits has become a major bottleneck and the need to increase testing throughput is rising. We aim to overcome these challenges by pooling samples together, and performing RNA extraction and RT-PCR in pools. Methods: We tested the efficiency and sensitivity of pooling strategies for RNA extraction and RT-PCR detection of SARS-CoV-2. We tested 184 samples both individually and in pools to estimate the effects of pooling. We further implemented Dorfman pooling with a pool size of eight samples in large-scale clinical tests. Results: We demonstrated pooling strategies that increase testing throughput while maintaining high sensitivity. A comparison of 184 samples tested individually and in pools of eight samples showed that test results were not significantly affected. Implementing the eight-sample Dorfman pooling to test 26 576 samples from asymptomatic individuals, we identified 31 (0.12%) SARS-CoV-2 positive samples, achieving a 7.3-fold increase in throughput. Discussion: Pooling approaches for SARS-CoV-2 testing allow a drastic increase in throughput while maintaining clinical sensitivity. We report the successful large-scale pooled screening of asymptomatic

Background-Studies that have combined accelerometers and global positioning systems (GPS) to identify walking have done so in carefully controlled conditions. This study tested algorithms for identifying walking trips from accelerometer and GPS data in free-living conditions. The study also assessed the accuracy of the locations where walking occurred compared to what participants reported in a diary.

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Transportation analysts have long recognized a role for the environment in travel behavior; techniques for incorporating the built environment into travel research remain in active development. This study uses multiple environmental representations to model automobile ownership and travel decisions with a single data set and model structure to test relationships already reported in the literature and to lay the foundation for extending this framework to additional travel modeling. Simple environmental measures, indices generated by factor analysis, and a neighborhood typology derived from cluster analysis of the factors, along with common household measures, are used to find the factors to provide information about travel that the clusters and direct measures do not. Automobile ownership and trips showed the expected relationships, with the former sensitive to sociodemographic factors and the latter sensitive also to the environment. Modes related differently to environmental factors; specifically, walk trips were strongly associated with accessibility and walkability, whereas drive trips were insensitive to these factors but were associated with other factors.

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